# Predicting point-of-care outcomes for text message crisis interventions in teens

> **NIH NIH R21** · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · 2024 · $220,694

## Abstract

Project Summary/Abstract
Over the last decade, suicidal ideation and suicide death have increased significantly across all age groups, but
have shown the most dramatic rise in teens and young adults. There is clear evidence that greater access to
mental health care is associated with reduced suicide risk, but increasing mental health care using conventional
models faces multiple barriers; ensuring access to mental health care in rural areas is especially challenging.
Accordingly, innovative approaches to provide mental health care, and especially crisis intervention, to teens are
essential.
 SafeUT is a text-messaging app developed in conjunction with state government agencies that links Utah
teens who have a mental health crisis to a counselor who can provide support, assess suicide risk, triage to
emergency services, and refer for additional treatment. Our project will utilize a large, rigorously anonymized
repository of text-message data (>130,000 encounters and >2.3 million messages) contained within SafeUT to
predict important outcomes of using the app, such as being referred to emergency services, staying
engaged with the counselor, and receiving a thorough risk assessment. We will examine SafeUT data with
cutting-edge machine-learning techniques, including natural language processing, to develop ways of
predicting SafeUT users’ outcomes. These predictive systems will, ultimately, be used to produce real-time
feedback systems that monitor and help improve the quality of SafeUT services.
 The current proposal squarely addresses Notice of Special Interest NOT-MH-22-110 (Priority
Research Opportunities in Crisis Response Services), which emphasizes the importance of projects
addressing crisis care in children and which requests applications focused on the development of “assessment
strategies and decision-making aides (e.g., predictive algorithms) that incorporate demographic, clinical…and
contextual data…to guide tailored strategies for resolution of distress, referral, and engagement in appropriate
follow-up services.” These are precisely the goals of the project described here. Evaluating SafeUT’s impact is
essential to improving the service over time through counselor training, policy changes, and in future, the
development of real-time decision-support systems that enhance counselors’ interventions.

## Key facts

- **NIH application ID:** 10789428
- **Project number:** 1R21MH135280-01
- **Recipient organization:** UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH
- **Principal Investigator:** Brent Michael Kious
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $220,694
- **Award type:** 1
- **Project period:** 2024-07-01 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10789428

## Citation

> US National Institutes of Health, RePORTER application 10789428, Predicting point-of-care outcomes for text message crisis interventions in teens (1R21MH135280-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10789428. Licensed CC0.

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